{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SPH\n",
    "\n",
    "[![Google Collab Book](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/tum-pbs/PhiFlow/blob/develop/examples/particles/SPH.ipynb)\n",
    "\n",
    "This example runs a simple SPH simulation based on Müller et al., Particle-based fluid simulation for interactive applications, 2003.\n",
    "The implementation is identical to the [corresponding example](https://github.com/NVIDIA/warp/blob/3ed2ceab824b65486c5204d2a7381d37b79fc314/warp/examples/core/example_sph.py) from Warp."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install --quiet phiflow\n",
    "from phi.torch.flow import *\n",
    "from phi.physics import sph\n",
    "# from phi.flow import *  # If JAX is not installed. You can use phi.torch or phi.tf as well.\n",
    "from tqdm.notebook import trange"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by defining our domain and fluid settings. Particles spawn in the lower left corner to simulate a dam break."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 216x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "domain = Box(x=80, y=80, z=80)\n",
    "smoothing_length = 0.8  # copied from warp, we would usually set desired_neighbors instead.\n",
    "initial_positions = pack_dims(math.meshgrid(x=25, y=100, z=25), spatial, instance('particles')) * smoothing_length\n",
    "initial_positions += 0.001 * smoothing_length * math.random_normal(initial_positions.shape)\n",
    "particles = Sphere(initial_positions, volume=smoothing_length**3, radius_variable=False)\n",
    "desired_neighbors = sph.expected_neighbors(particles.volume, smoothing_length, 3)\n",
    "\n",
    "particle_mass = 0.01 * math.mean(particles.volume)\n",
    "dt = 0.01 * smoothing_length\n",
    "dynamic_visc = 0.025\n",
    "damping_coef = -0.95\n",
    "gravity = vec(x=0, y=0, z=-0.1)\n",
    "pressure_normalization = -(45.0 * particle_mass) / (PI * smoothing_length**6)\n",
    "viscous_normalization = (45.0 * dynamic_visc * particle_mass) / (PI * smoothing_length**6)\n",
    "\n",
    "plot([domain, initial_positions], overlay='list', alpha=[.1, .4], size=(3, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the SPH update step.\n",
    "The `sph.neighbor_graph` function builds a sparse connectivity matrix and evaluates the specified kernel `poly6`.\n",
    "The neighbors of each particle are listed along the corresponding dual dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "@jit_compile\n",
    "def sph_step(v: Field, dt=dt, isotropic_exp=20., base_density=1.):\n",
    "    graph = sph.neighbor_graph(v.geometry, 'poly6', desired_neighbors=desired_neighbors, compute='kernel', domain=domain)\n",
    "    # --- compute density and pressure ---\n",
    "    rho = math.sum(graph.edges['kernel'], dual) * particle_mass  # this includes the density contribution from self\n",
    "    pressure = isotropic_exp * (rho - base_density)\n",
    "    nb_rho = rename_dims(rho, instance, dual)\n",
    "    nb_pressure = rename_dims(pressure, instance, dual)\n",
    "    distance_gaps = smoothing_length - graph.distances\n",
    "    # --- pressure force ---\n",
    "    avg_pressure = (graph.connectivity * pressure + nb_pressure) / (2 * nb_rho)\n",
    "    pressure_force = -graph.unit_deltas * avg_pressure * distance_gaps ** 2\n",
    "    # --- viscosity force ---\n",
    "    dv = math.pairwise_differences(v.values, format=graph.edges)\n",
    "    viscous_force = dv / nb_rho * distance_gaps\n",
    "    # --- sum forces, integrate ---\n",
    "    force = math.sum(pressure_normalization * pressure_force + viscous_normalization * viscous_force, dual)\n",
    "    a = force / rho + gravity\n",
    "    v += a * dt  # kick\n",
    "    return v.shifted(dt * v.values)\n",
    "\n",
    "@jit_compile\n",
    "def apply_bounds(v: Field, damping_coef=-0.95) -> Field:\n",
    "    clipped = math.clip(v.points, domain.lower, domain.upper)\n",
    "    v = field.where(clipped == v.points, v, v * damping_coef)\n",
    "    return v.shifted_to(clipped)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run the simulation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b9eb5ae6e53040e4b06d07ad2e5489a3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/480 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "initial_state = Field(particles, vec(x=0, y=0, z=0), 0)\n",
    "trj = iterate(lambda v: apply_bounds(sph_step(v)), batch(t=480), initial_state, substeps=int(32 / smoothing_length), range=trange)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<matplotlib.animation.FuncAnimation at 0x26babae1820>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot([domain, trj.points.t[::3]], overlay='list', alpha=[.1, .3], animate='t', size=(5, 5), frame_time=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also plot or animate 2D projections of the simulation.\n",
    "This is much faster than plotting a 3D sphere for each particle with Matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(trj.geometry.t[150]['x,z'], overlay='list', size=(5, 5), frame_time=50)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "phiflow2",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
